Qwen3-Coder-Next-TQ-4bit

80B-parameter Mixture-of-Experts coding model · 3B activated parameters · TextCLF Quant (TQ) 4-bit

Note on Hugging Face's model-size display: Hugging Face may report a smaller stored parameter count for this repository because TQ stores the weights in a packed 4-bit representation. The underlying model is Qwen3-Coder-Next, with approximately 80B total parameters and 3B activated parameters per token. The smaller number reflects the packed storage tensors counted by the Hub, not the parameter count of the original model architecture.

Qwen3-Coder-Next quantized to 4-bit with TextCLF Quant (TQ).

TextCLF Quant: https://textclf.com

TQ is a calibration-free 4-bit quantization method designed to preserve the behavior of the original model without requiring a calibration dataset. Because TQ does not optimize the quantization around a fixed calibration corpus, it is designed to generalize beyond calibration-specific data and workloads.

This repository contains Qwen3-Coder-Next quantized with TextCLF Quant (TQ) 4-bit.

Why TQ?

Calibration-free. TQ does not require a representative calibration dataset before quantization.

This avoids tying the quantization procedure to a particular calibration corpus and is intended to provide better generalization beyond the data that would otherwise have been used for calibration.

4-bit inference. Model weights are quantized to 4-bit for substantially lower weight memory requirements than BF16/FP16 deployment.

Native vLLM integration. TQ models run through the TQ vLLM quantization plugin and custom CUDA kernels included in the TextCLF TQ Docker image.

Model

Base model	Qwen/Qwen3-Coder-Next
Original model parameters	~80B total / ~3B activated
Model type	Mixture-of-Experts coding model
Quantization	TextCLF Quant (TQ) 4-bit
Calibration	None — calibration-free
Runtime	vLLM + TQ custom kernels
Quantization name	tq
Context length	262,144 tokens

Run with Docker

The recommended way to run this model is with the TQ Docker image, which contains the compatible vLLM installation, TQ quantization plugin, and TQ CUDA kernels.

1. Make sure NVIDIA Docker support works

docker run --rm --gpus all \
  nvidia/cuda:13.0.2-base-ubuntu22.04 \
  nvidia-smi

Your GPU should appear in the output.

2. Start Qwen3-Coder-Next-TQ-4bit

docker run --rm --gpus all \
  -p 8000:8000 \
  docker.io/textclf/tq-quant:4bit-main \
  vllm serve textclf/Qwen3-Coder-Next-TQ-4bit \
  --quantization tq

The model is then available through vLLM's OpenAI-compatible API on port 8000.

WSL2

If you are running Docker through WSL2 and vLLM reports that UVA is unavailable, enable vLLM's WSL2 pinned-memory support:

docker run --rm --gpus all \
  -e VLLM_WSL2_ENABLE_PIN_MEMORY=1 \
  -p 8000:8000 \
  docker.io/textclf/tq-quant:4bit-main \
  vllm serve textclf/Qwen3-Coder-Next-TQ-4bit \
  --quantization tq

Send a request

Once the server is ready:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "textclf/Qwen3-Coder-Next-TQ-4bit",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that performs binary search on a sorted list."
      }
    ]
  }'

Tool calling

Qwen3-Coder-Next is designed for coding and agentic workflows and can be served with vLLM's automatic tool-choice support.

Example:

docker run --rm --gpus all \
  -p 8000:8000 \
  docker.io/textclf/tq-quant:4bit-main \
  vllm serve textclf/Qwen3-Coder-Next-TQ-4bit \
  --quantization tq \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder

Hugging Face authentication

For higher Hugging Face Hub rate limits, pass your Hugging Face token into the container:

docker run --rm --gpus all \
  -e HF_TOKEN="$HF_TOKEN" \
  -p 8000:8000 \
  docker.io/textclf/tq-quant:4bit-main \
  vllm serve textclf/Qwen3-Coder-Next-TQ-4bit \
  --quantization tq

On WSL2, combine it with:

-e VLLM_WSL2_ENABLE_PIN_MEMORY=1

About TextCLF Quant (TQ)

Learn more at https://textclf.com.

TextCLF Quant (TQ) is a calibration-free quantization approach for efficient LLM inference.

Traditional post-training quantization methods can depend on a calibration dataset to estimate quantization parameters. The resulting quantization can therefore be influenced by the distribution and composition of that calibration data.

TQ removes that calibration-data requirement. The goal is to retain strong fidelity to the original model while allowing the quantized representation to generalize beyond any particular calibration corpus.

Base model

This checkpoint is derived from Qwen/Qwen3-Coder-Next, a Mixture-of-Experts coding model designed for software development, agentic coding, tool use, and long-context workflows.

Please refer to the original Qwen model card for architecture details, capabilities, usage guidance, limitations, and upstream licensing information.

License

The base model is released under the Apache 2.0 License. See the repository license and the original Qwen model card for applicable terms.